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Idea density for predicting Alzheimer's disease from transcribed speech.

, , and . CoNLL, page 322-332. Association for Computational Linguistics, (2017)

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Do POS Tags Help to Learn Better Morphological Segmentations?, and . ALTA, page 91-100. ACL, (2015)Simplifying the Classification of App Reviews Using Only Lexical Features., , and . ICSOFT (Selected Papers), volume 1077 of Communications in Computer and Information Science, page 173-193. Springer, (2018)Noisy-Channel Spelling Correction Models for Estonian Learner Language Corpus Lemmatisation.. Baltic HLT, volume 247 of Frontiers in Artificial Intelligence and Applications, page 213-220. IOS Press, (2012)Enhancing Sequence-to-Sequence Neural Lemmatization with External Resources., and . EACL, page 3112-3122. Association for Computational Linguistics, (2021)Lexicon-Enhanced Neural Lemmatization for Estonian., and . Baltic HLT, volume 328 of Frontiers in Artificial Intelligence and Applications, page 158-165. IOS Press, (2020)Comparison of Current Approaches to Lemmatization: A Case Study in Estonian., and . NoDaLiDa, page 280-285. University of Tartu Library, (2023)Modeling Composite Labels for Neural Morphological Tagging., and . CoNLL, page 368-379. Association for Computational Linguistics, (2018)Improving Topic Coherence with Latent Feature Word Representations in MAP Estimation for Topic Modeling., , and . ALTA, page 116-121. ACL, (2015)Is the SAFE Approach Too Simple for App Feature Extraction? A Replication Study., , and . REFSQ, volume 11412 of Lecture Notes in Computer Science, page 21-36. Springer, (2019)Your Model Is Not Predicting Depression Well And That Is Why: A Case Study of PRIMATE Dataset., , and . CoRR, (2024)